Tumours develop through the accumulation of mutations in DNA. Recent advances in high-throughput (next-generation) DNA sequencing allow researchers rapid identification of mutations in a genome. This has increased understanding of the biology of cancer cells and has led to more effective drugs and better predictions of patient outcomes.
However, maximizing the clinical use of next-generation sequencing data requires sophisticated software to improve the analysis of genomes and identification of mutant sequences related to tumours. These large datasets can contain errors that distort and obscure the true biological signals. Consequently, sophisticated computational approaches are required to maximize the clinical utility of next generation datasets, and to advance the field of cancer genomics. And even when these approaches are created, robust software must be created to make them broadly applicable.
Drs. Sohrab Shah and Paul C. Boutros developed innovative software that will improve patient care by identifying and analyzing the mutations involved in cancer progression. Software tools have been available to the broader cancer genomics community and have been applied in various research settings to over 1000 tumours. Significant developments include the development and publication of several new algorithms for cancer genome analysis including TITAN, PyClone, xseq, and Kronos.
Software developed from this project has been applied in several landmark studies across a range of cancers: lymphoma, prostate, breast, ovarian and in diverse experimental designs. By the end this project published over 20 articles in high impact journals with many submitted or in preparation and leveraged over $60M funding.